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A Model Using Household-Income and Household-Consumption Data to Estimate the Cost and the Effectiveness of Subsidies
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A Model Using Household-Income and Household-Consumption Data to Estimate the Cost and the Effectiveness of Subsidies

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GHME 2013 Conference …

GHME 2013 Conference
Session: New directions in cost-effectiveness analysis
Date: June 17 2013
Presenter: Lorenzo Sabatelli
Institute: GLOB MOD

Published in: Economy & Finance, Technology

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  • 1. GLOB MOD A Model Using Household-Income and Household-Consumption Data to Estimate the Cost and the Effectiveness of Subsidies Lorenzo Sabatelli GLOB MOD Dean T. Jamison University of Washington
  • 2. GLOB MOD Consumer Choice, Health Risk, and Policy Global Health-Risk Factors Childhood underweight Household air pollution from solid fuel High blood pressure Suboptimal breastfeeding Tobacco smoking and second hand smoking Ambient particulate matter pollution Diet low in fruits Iron deficiency High fasting plasma glucose High body-mass index Diet high in sodium Alcohol use Unimproved sanitation High total cholesterol Diet low in nuts and seeds Vitamin A deficiency Diet low in vegetables Diet low in grains Zinc deficiency Unimproved water source People do not choose to become ill, choose to be happy Individuals and Households make choices on the purchase of goods and services They try to optimize the use of available resources to maximize the satisfaction of their perceived needs (Maximization of Utility) Some choices may be optimal, but not for health  Governments can use financial instruments, such as subsidies, to influence consumer choices Subsidies decrease the out-of-pocket price of certain goods to the consumer, increasing demand
  • 3. GLOB MOD Data on Household Budget Allocation Data on Household Income and Consumption Data on Utility and Income Cost and Effectiveness of Subsidizing the Installation of Household-Based Sanitations in Rural India Case-Study MODEL Cost and Effectiveness of Subsidizing the Purchase of Health-Relevant Goods Overview Do not reproduce without the express written consent of the authors
  • 4. GLOB MOD Parameter Intuitive Definition Income Elasticity of Demand The percent change in the demand for a class of goods (e.g. food, clothes, housing) due to a 1% increase in income, when prices remain constant. Uncompensated Own-Price Elasticity of Demand The percent change in the demand for a class of goods (e.g. food, clothes, housing) due to a 1% increase in the average price, when income and the price of other goods remain constant. Budget Share The proportion of a consumer budget used to purchase goods of the class of interest. Quantities of Interest
  • 5. GLOB MOD Elasticity of the Marginal Utility of Income
  • 6. GLOB MOD The Model Recipe BUDGET SHARE OF GOOD X INCOME ELASTICITY OF DEMAND OF GOOD X ELASTICITY OF THE MARGINAL UTILITY OF INCOME Price Elasticity of Household-Based Sanitations in Rural India UNCOMPENSATED OWN PRICE ELASTICITY OF DEMAND OF GOOD X Do not reproduce without the express written consent of the authors
  • 7. GLOB MOD  Utility increases with income, with decreasing margins (convexity)  Consumers allocate their own budget to the purchase of goods and services in a way that maximizes utility  Demand adjusts to changes in prices and in income in a very short (negligible) time  Preference Independence: the utility associated with the consumptions of one class of goods does not depend on the consumption of goods of a different class (no interaction) These assumptions are the basis of the so called Florida Preference Independence Model Model Assumptions
  • 8. GLOB MOD Uncompensated Own-Price Elasticity vs. Income Elasticity Model Assumptions Demand Equation Relationship between price-elasticity and income-elasticity To protect the confidentiality of unpublished results this slide is different from the original used in the GHME 2013 conference Price Elasticity = function (Income Elasticity; Budget Share; Elasticity of the Marginal Utility)
  • 9. GLOB MOD Background  60% - 70% of households without latrines  Absence of improved sanitations is a major public-health risk factor Data Sources  District Level Household and Facility Survey (DLHS) provides health care and utilization indicators at the district level  Asian Development Bank statistics and policy documents APPLICATION Subsidizing the Installation of Sanitations in Rural India
  • 10. GLOB MOD What is Income, by the way? Permanent-income was estimated on a log- scale with a random-effects probit model using household asset ownership as a (probabilistic) proxy for household income Calculation of Income, Income Elasticity and Other Parameters Calculating the income elasticity with a Mixed-Effect First Difference Regression using DLHS-2 and DLHS-3 data Parameter Value Annual cost of one basic improved-sanitation facility 5.5-11 US$ Current sanitation coverage ~32% Total number of households 140 millions Additional parameters To protect the confidentiality of unpublished results this slide is slightly different from the original used in the GHME 2013 conference
  • 11. GLOB MOD Results Additional Number of Households with Sanitations Annual Cost to the Policy Maker Please note: These results are preliminary. Do not reproduce without the express written consent of the authors
  • 12. GLOB MOD  Within the framework of the Florida Preference Independence Model, a relationship exists between the uncompensated own price elasticity of demand and the income elasticity of demand for a given class of market goods.  When used to study the impact of subsidizing sanitations in rural India, the model provides probabilistic estimates of the achievable increase in coverage and of the potential cost to the government, as a function of the subsidized fraction of the price  The proposed approach may provide a fast and inexpensive method for broad brush assessments that could help design policies and set public-health priorities.  Future developments are likely to involve: technical improvements of the model here presented; expansion to other financial instruments; and developing user-friendly software tools for simulating the impact of financial instruments in health policy. Conclusions and Future Developments